Machine learning algorithms application in COVID-19 disease: a systematic literature review and future directions

dc.creatorSalcedo, Dixon
dc.creatorGuerrero Santander, Cesar Dario
dc.creatorSaeed, Khalid
dc.creatorMardini, Johan
dc.creatorCalderón-Benavides, Liliana
dc.creatorHenríquez, Carlos
dc.creatorMendoza, Andrés
dc.date2023-05-17T23:29:00Z
dc.date2023-05-17T23:29:00Z
dc.date2022-12-03
dc.date.accessioned2023-10-03T19:24:50Z
dc.date.available2023-10-03T19:24:50Z
dc.identifierSalcedo, D.; Guerrero, C.; Saeed, K.; Mardini, J.; Calderon-Benavides, L.; Henriquez, C.; Mendoza, A. Machine Learning Algorithms Application in COVID-19 Disease: A Systematic Literature Review and Future Directions. Electronics 2022, 11, 4015. https://doi.org/10.3390/electronics11234015
dc.identifierhttps://hdl.handle.net/11323/10139
dc.identifier10.3390/electronics11234015
dc.identifier2079-9292
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9170009
dc.descriptionSince November 2019, the COVID-19 Pandemic produced by Severe Acute Respiratory Syndrome Severe Coronavirus 2 (hereafter COVID-19) has caused approximately seven million deaths globally. Several studies have been conducted using technological tools to prevent infection, to prevent spread, to detect, to vaccinate, and to treat patients with COVID-19. This work focuses on identifying and analyzing machine learning (ML) algorithms used for detection (prediction and diagnosis), monitoring (treatment, hospitalization), and control (vaccination, medical prescription) of COVID-19 and its variants. This study is based on PRISMA methodology and combined bibliometric analysis through VOSviewer with a sample of 925 articles between 2019 and 2022 derived in the prioritization of 32 papers for analysis. Finally, this paper discusses the study’s findings, which are directions for applying ML to address COVID-19 and its variants.
dc.format24 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.publisherSwitzerland
dc.relationElectronics
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dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rightsAtribución 4.0 Internacional (CC BY 4.0)
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourcehttps://www.mdpi.com/2079-9292/11/23/4015
dc.subjectCOVID-19
dc.subjectMachine learning
dc.subjectPrediction algorithms
dc.subjectMortality prediction
dc.titleMachine learning algorithms application in COVID-19 disease: a systematic literature review and future directions
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_dcae04bc
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typehttp://purl.org/redcol/resource_type/ARTREV
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/coar/version/c_970fb48d4fbd8a85


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